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Ornith-9B-mlx-nvfp4
srv-sngh/Ornith-9B-mlx-nvfp4
Ornith-9B-mlx-nvfp4 is a image-text-to-text model from srv-sngh. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for mlx. The card lists the license as mit.
A mixed-precision nvfp4 (group 16) quantization of deepreinforce-ai/Ornith-1.0-9B, a Qwen3.5-class hybrid vision-language model.
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From the Hugging Face model README
Ornith-1.0-9B - MLX nvfp4 (complete VLM, Krill-native)
A mixed-precision nvfp4 (group 16) quantization of deepreinforce-ai/Ornith-1.0-9B, a Qwen3.5-class hybrid vision-language model.
Original model and weights by deepreinforce-ai (Ornith-1.0-9B). Full credit to them; this repo only re-quantizes their model.
Why this build
- 👁️ Complete vision-language model, the vision tower is included. Many community MLX/quantized Ornith builds are text-only (vision stripped). This one keeps the full VLM, so it does image + text, not just text.
- 🎯 nvfp4 mixed precision. The decoder is nvfp4 at group size 16, with
down_projando_projprotected at 8-bit and the vision tower kept at higher precision. Smaller and faster than int4 at comparable quality. - ⚡ Native Krill runtime (Krill v0.14.1). Runs as a native Swift + MLX model on Apple Silicon. Krill ships a from-scratch native runtime for Ornith's hybrid GatedDeltaNet (SSM) + attention decoder, not just an mlx_vlm passthrough.
- ✅ Parity-verified. Text decoder matches mlx_vlm token-for-token on the reference checkpoint.
- 💻 Fits a 24 GB Apple-silicon box at ~6.4 GB.
Run in Krill (recommended)
# install Krill
brew tap srvsngh99/krill && brew install krill
# or:
curl -fsSL https://raw.githubusercontent.com/srvsngh99/Krill/main/install.sh | sh
# run Ornith nvfp4 (pulls this repo)
krill run ornith-9b-nvfp4 "Give three tips for staying focused while studying."
# keep Krill up to date
krill update
Run with mlx_vlm (text + vision)
pip install -U mlx-vlm
python -m mlx_vlm generate --model srv-sngh/Ornith-9B-mlx-nvfp4 \
--prompt "Describe this image." --image path/to/image.jpg --max-tokens 200
About Ornith-1.0-9B
A Qwen3.5-class hybrid VLM: the text decoder is a Qwen3-Next-style stack interleaving GatedDeltaNet linear-attention (SSM) layers with full softmax-attention every fourth layer, plus a vision tower. Full credit to the original creators, deepreinforce-ai.
Quantization
| field | value |
|---|---|
| format | MLX nvfp4 (mixed precision) |
| group size | 16 |
| protected | down_proj, o_proj @ 8-bit; vision tower at higher precision |
| size | ~6.4 GB |
| contents | complete VLM (text decoder + vision tower) |
In Krill, the text decoder runs natively; the vision tower currently runs via mlx_vlm (native vision is a follow-up).
License
MIT, matching the base model deepreinforce-ai/Ornith-1.0-9B.
About this build
Quantized and tested by Sourav Singh for Krill, a Mac-native LLM runtime and coding agent in one Swift + MLX binary. More Apple Silicon builds: huggingface.co/srv-sngh. Notes on building with local models and frontier AI: souravailabs.ai · GitHub · LinkedIn · YouTube
- Task
- Image-Text-to-Text
- Library
- mlx
- Type
- qwen3_5
- License
- mit
- Created
- Jun 27, 2026
- Updated
- Sep 5, 2026